A novel approach for data-driven process and condition monitoring systems on the example of mill-turn centers

Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device- and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the aggregation of control signals and process variables. Further challeng...

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Bibliographic Details
Published in:Production engineering (Berlin, Germany) Vol. 12; no. 3-4; pp. 525 - 533
Main Authors: Kißkalt, Dominik, Fleischmann, Hans, Kreitlein, Sven, Knott, Manuel, Franke, Jörg
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018
Springer Nature B.V
Subjects:
Communication networks
Computer architecture
Cyber-physical systems
Engineering
Industrial and Production Engineering
Industrial Internet of Things
Machine learning
Machinery condition monitoring
Machining centres
Markov chains
Pattern recognition
Probabilistic models
Process variables
Production
Quality Assurance
Structure borne noise
Topology
Unsupervised learning
Condition monitoring systems
Cyber-physical systems
Machine tools
Smart factory
Unsupervised learning
Machine learning
ISSN:0944-6524, 1863-7353
Online Access:Get full text
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Summary:Implementing condition monitoring functionality in production machinery often proves to be a difficult task. Device- and process-specific algorithms must be created while inhomogeneous industrial communication networks hinder the aggregation of control signals and process variables. Further challenges arise from the advance of flexible cyber-physical systems (CPS) and the industrial internet of things (IIoT). They demand a service-oriented condition monitoring architecture, which seamlessly adapts to quickly changing production topologies. In this context, data-driven systems which are capable of unsupervised learning are promising approaches. The aim is the autonomous identification of significant process variables and patterns. This paper describes a machine learning approach for a condition and process monitoring system on the basis of pattern recognition within structure-borne noise of rotating cutting machinery. Process states are defined under application of non-negative matrix factorization (NMF). A production model is learned and deployed on the basis of Gaussian mixture models (GMM) and hidden Markov models (HMM) in a two stage process. Additionally a generic framework to ease the implementation of decentralized condition monitoring functionalities is given. A decentralized component, the monitoring module, constitutes a part of a holistic condition monitoring architecture managed by a central server. The approach is evaluated on the example of mill-turn centers.
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ISSN:0944-6524
1863-7353
DOI:10.1007/s11740-018-0797-0